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The estimation methodology so far is linear least squares.
First we found a spatial predictor on a mesh.
Then we introduce missing data by interlacing the mesh on both axes.
Then we assert the two-dimensional filter is a dip filter
and that it is appropriate for use on the doubly refined mesh.
With this operator,
we then solve the linear least squares problem for the missing data.
We made an approximation when we presumed the 2D filter
was essentially a dip filter and that the imperfect coherency
was not significant.
Now is the time to patch up that approximation
by solving the nonlinear least-squares problem
allowing both the filter and the missing data to readjust
on the principle that the output power be minimized.
Presumeably we are already close to the solution
so we can expect rapid convergence.

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** Up:** INTERPOLATION WITH SPATIAL PREDICTORS
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Stanford Exploration Project

1/13/1998